LCA of waste management systems: Development of tools for modeling and uncertainty analysis

1 Department of Environmental Engineering, Technical University of Denmark2 Residual Resource Engineering, Department of Environmental Engineering, Technical University of Denmark3 Department of Applied Mathematics and Computer Science, Technical University of Denmark4 Software Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark

Abstract:

Since the late 1990s, life cycle assessment (LCA) has been increasingly applied to waste management to quantify direct, indirect and avoided impacts from various treatment options. The construction of inventories for waste management systems differs from classical product-LCAs in that (1) these systems usually handle a heterogeneous mix of different waste fractions, (2) optimal treatments differ for these various fractions due to their chemical and physical properties and (3) emissions from final disposal places may occur over a very long time, depending on technology choice, and thus they have to be modelled rather than monitored as in classical LCA (e.g. landfilling or the application of processed waste on agricultural land). Therefore LCA-tools are needed which specifically address these issues and enable practitioners to model properly their systems. In this thesis several pieces of work are presented. First a review was carried out on all LCA studies of waste management systems published before mid-2012. This provided a global overview of the technologies and waste fractions which have attracted focus within LCA while enabling an analysis of methodological tendencies, the use of tools and databases and the application of uncertainty analysis methods. The major outcome of this thesis was the development of a new LCA model, called EASETECH, building on the experience with previous LCA-tools, in particular the EASEWASTE model. Before the actual implementation phase, a design phase involved a thorough analysis of requirements and the implementation of a conceptual model as a computational prototype, to ensure the feasibility of the model. During the development process, focus has been primarily placed on: • Providing a toolbox of processes to model the different transfer functions found in waste treatment technologies. These material transfer functions specify how substances in input flows are transferred to output flows and environmental compartments and include for example processes for anaerobic digestion or landfill gas generation. • Offering a flexible user interface where the user can connect freely all processes and combine them to build new treatment technologies and eventually scenarios. • Keeping track of waste flows, throughout entire scenarios, as matrixes of fractions and chemical and physical properties. Displaying the time dimension of flows when needed, e.g. for gas and leachate emissions from landfill. • Offering import functions which enable the use of newly released databases and life cycle impact assessment methods. • Providing tools for uncertainty analysis. Furthermore, as the review pointed out the lack of quantitative assessment of uncertainties in waste-LCA studies, a systematic approach was developed which includes several steps: sensitivity analysis, uncertainty propagation, uncertainty contribution analysis and combined sensitivity analysis. The result from each proposed step narrows the scope of the following step while producing a communicable outcome for decision makers. This method permits an analysis of the system at different scopes, from the largest picture with all processes and impact categories to a more detailed analysis of the reasons and probability for a shift in rankings between scenarios. To help practitioners in the screening of sources of uncertainty, a description of all uncertainties usually encountered in waste-LCAs was also provided. Finally, an insight into uncertainty representation was presented which highlighted the importance of the choice of uncertainty representation, by comparing the propagation of probability distributions and fuzzy sets in a case study. A method was suggested whereby the practitioner is invited to choose one of the two representation types for each parameter, based on the level of information available, and all parameter uncertainties are propagated jointly. The use of the new EASETECH model on two case studies has demonstrated the transparency of the model (allowing for a clear overview of all flows and data inputs), its flexibility (through the modelling of a full wastewater treatment plant) and the usefulness of the uncertainty analysis methods implemented. Further developments will focus on tools for economic analysis, an improved graphical display of results, the design of new process templates, the provision of an external editor of process templates and the development of new functionalities for the impact assessment phase.